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Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage
Kengo Oka1, Saki Uemura1, Satoru Morishita1
1Advanced Oral Health Science Research Laboratories, Research and Development Headquarters, LION Corporation, Edogawa-ku, Tokyo, Japan.
Plos One
|December 9, 2025
Summary
AI models can detect malocclusion in children during mixed dentition. Dental images were key, with oral habits also important for certain types, showing promise for early screening.
Area of Science:
- Dentistry
- Artificial Intelligence
- Machine Learning
Background:
- Malocclusion is a common dental anomaly requiring early detection and intervention.
- A tool to aid early dentition assessment and patient motivation is needed.
Purpose of the Study:
- To assess the feasibility of developing an AI model for detecting malocclusion in children during the mixed dentition stage.
Main Methods:
- A feasibility study utilized cross-sectional data from 519 elementary school children.
- AI binary classification models were developed using an automated machine learning platform to identify deep bite, maxillary protrusion, and crowding.
- Model performance was evaluated using metrics like accuracy, precision, F1 score, and AUC.
Main Results:
- The deep bite classification model achieved high accuracy (AUC > 0.90), while maxillary protrusion and crowding models showed moderate accuracy (AUC > 0.70).
- Dental images were the most significant factor for all models.
- Oral habit questionnaire data were important for maxillary protrusion and crowding detection, showing a significant association.
Conclusions:
- AI-based binary classification models show potential as effective screening tools for detecting malocclusion in mixed dentition.
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